A four-group urine risk classifier for predicting outcomes in patients with prostate cancer

• Type of Material: Article
• Authors: Shea P. Connell, University of East Anglia; Marcelino Yazbek-Hanna, University of East Anglia; Frank McCarthy, Institute of Cancer Research, Sutton UK; Rachel Hurst, University of East Anglia; Martyn Webb, University of East Anglia; Helen Curley, University of East Anglia; Helen Walker, Norfolk and Norwich University Hospitals; Rob Mills, Norfolk and Norwich University Hospitals; Richard Y. Ball, Norfolk and Norwich University Hospitals; Martin Sanda, Emory University; Kathryn L. Pellegrini, Emory University; Dattatraya Patil, Emory University; Antoinette S. Perry, University College Dublin; Jack Schalken, Radboud University Nijmegen; Hardev Pandha, University of Surrey; Hayley Whitaker, University College London; Nening Dennis, Institute of Cancer Research, Sutton; Christine Stuttle, Institute of Cancer Research, Sutton; Ian G. Mills, Queens University Belfast; Ingrid Guldvik, University of Oslo
• Language: English
• Date: 2019-10-01
• Publisher: Wiley
• Publication Version: Final Published Version
• Copyright Statement: © 2019 The Authors
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1111/bju.14811
• Title of Journal or Parent Work: BJU International
• ISSN: 1464-4096
• Volume: 124
• Issue: 4
• Start Page: 609
• End Page: 620
• Grant/Funding Information: This study was possible thanks to the Movember Foundation GAP1 Urine Biomarker project, The Masonic Charitable Foundation, The Bob Champion Cancer Trust, the King family, The Andy Ripley Memorial Fund and the Stephen Hargrave Trust
• Keywords: BIOPSY; urine; biomarker; Science & Technology; ANTIGEN; Urology & Nephrology; GENE-EXPRESSION; active surveillance; RADICAL PROSTATECTOMY; ACTIVE SURVEILLANCE; DIAGNOSIS; #PCSM; MORTALITY; SIGNATURE; #ProstateCancer; Life Sciences & Biomedicine; cell free; MODELS; RADIATION; liquid biopsy
• Research Categories: Biology, Genetics; Health Sciences, Medicine and Surgery; Health Sciences, Public Health

Abstract

Objectives: To develop a risk classifier using urine-derived extracellular vesicle (EV)-RNA capable of providing diagnostic information on disease status prior to biopsy, and prognostic information for men on active surveillance (AS). Patients and Methods: Post-digital rectal examination urine-derived EV-RNA expression profiles (n = 535, multiple centres) were interrogated with a curated NanoString panel. A LASSO-based continuation ratio model was built to generate four prostate urine risk (PUR) signatures for predicting the probability of normal tissue (PUR-1), D'Amico low-risk (PUR-2), intermediate-risk (PUR-3), and high-risk (PUR-4) prostate cancer. This model was applied to a test cohort (n = 177) for diagnostic evaluation, and to an AS sub-cohort (n = 87) for prognostic evaluation. Results: Each PUR signature was significantly associated with its corresponding clinical category (P < 0.001). PUR-4 status predicted the presence of clinically significant intermediate- or high-risk disease (area under the curve = 0.77, 95% confidence interval [CI] 0.70–0.84). Application of PUR provided a net benefit over current clinical practice. In an AS sub-cohort (n = 87), groups defined by PUR status and proportion of PUR-4 had a significant association with time to progression (interquartile range hazard ratio [HR] 2.86, 95% CI 1.83–4.47; P < 0.001). PUR-4, when used continuously, dichotomized patient groups with differential progression rates of 10% and 60% 5 years after urine collection (HR 8.23, 95% CI 3.26–20.81; P < 0.001). Conclusion: Urine-derived EV-RNA can provide diagnostic information on aggressive prostate cancer prior to biopsy, and prognostic information for men on AS. PUR represents a new and versatile biomarker that could result in substantial alterations to current treatment of patients with prostate cancer.